SoroushVahidi/scidocs huggingface dataset:
Validation of the Dutch Version of the CDC Core Healthy Days Measures in a Community Sample
(2005)

An important disadvantage of most indicators of health related quality of life used in public health surveillance is their length. In this study the authors investigated the reliability and validity of a short indicator of health related quality of life, the Dutch version of the four item ‘CDC Core Healthy Days Measures’ (CDC HRQOL-4). The reliability was evaluated by calculating Cronbach’s alpha of the CDC HRQOL-4. The concurrent validity was tested by comparing the CDC HRQOL-4 with three other indicators of health related quality of life, the SF-36, the WHOQoL-BREF and the GHQ-12. The construct validity was evaluated by assessing the ability of the CDC HRQOL-4 to discriminate between respondents with and without a (non-mental) chronic condition, depression, a visit to the general practitioner and use of prescription drugs. Randomly sampled respondents from the city of Utrecht were asked to fill in a questionnaire. 659 respondents (response rate 45%) completed the questionnaire. Participants represented the adult, non-institutionalised population of the city of Utrecht, the Netherlands: 58% women; mean age 41 years; 15% of non-Dutch origin. The reliability of the CDC HRQOL-4 was good. Cronbach’s alpha of three of the four CDC HRQOL-4-items was 0.77 which is good for internal consistent scales. The concurrent validity was good. The four items of the CDC HRQOL-4 showed higher correlations with their corresponding domains of the other instrument than the other domains. Comparison of respondents with or without a chronic condition, depression, visit to the GP and use of prescription drugs produced evidence for an excellent construct validity of the CDC HRQOL-4.
Hardware/Software Codesign: The Past, the Present, and Predicting the Future
(2012)

Hardware/software codesign investigates the concurrent design of hardware and software components of complex electronic systems. It tries to exploit the synergy of hardware and software with the goal to optimize and/or satisfy design constraints such as cost, performance, and power of the final product. At the same time, it targets to reduce the time-to-market frame considerably. This paper presents major achievements of two decades of research on methods and tools for hardware/software codesign by starting with a historical survey of its roots, by highlighting its major research directions and achievements until today, and finally, by predicting in which direction research in codesign might evolve in the decades to come.
Analysis of Audio-Visual Features for Unsupervised Speech Recognition
(2017)

Research on “zero resource” speech processing focuses on learning linguistic information from unannotated, or raw, speech data, in order to bypass the expensive annotations required by current speech recognition systems. While most recent zero-resource work has made use of only speech recordings, here, we investigate the use of visual information as a source of weak supervision, to see whether grounding speech in a visual context can provide additional benefit for language learning. Specifically, we use a dataset of paired images and audio captions to supervise learning of low-level speech features that can be used for further “unsupervised” processing of any speech data. We analyze these features and evaluate their performance on the Zero Resource Challenge 2015 evaluation metrics, as well as standard keyword spotting and speech recognition tasks. We show that features generated with a joint audiovisual model contain more discriminative linguistic information and are less speaker-dependent than traditional speech features. Our results show that visual grounding can improve speech representations for a variety of zero-resource tasks.
Organizational behavior: affect in the workplace.
(2002)

The study of affect in the workplace began and peaked in the 1930s, with the decades that followed up to the 1990s not being particularly fertile. Whereas job satisfaction generally continues to be loosely but not carefully thought of and measured as an affective state, critical work in the 1990s has raised serious questions about the affective status of job satisfaction in terms of its causes as well as its definition and measurement. Recent research has focused on the production of moods and emotions at work, with an emphasis, at least conceptually, on stressful events, leaders, work groups, physical settings, and rewards/punishment. Other recent research has addressed the consequences of workers' feelings, in particular, a variety of performance outcomes (e.g., helping behaviors and creativity). Even though recent interest in affect in the workplace has been intense, many theoretical and methodological opportunities and challenges remain.
Personality Traits Recognition on Social Network - Facebook
(2013)

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
(2009)

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
A Hybrid Gripper With Soft Material and Rigid Structures
(2019)

Various robotic grippers have been developed over the past several decades for robotic manipulators. Especially, the soft grippers based on the soft pneumatic actuator (SPA) have been studied actively, since it offers more pliable bending motion, inherent compliance, and a simple morphological structure. However, few studies have focused on simultaneously improving the fingertip force and actuation speed within the specified design parameters. In this study, we developed a hybrid gripper that incorporates both soft and rigid components to improve the fingertip force and actuation speed simultaneously based on three design principles: first, the degree of bending is proportional to the ratio of the rigid structure; second, a concave chamber design is preferred for large longitudinal strain; and third, a round shape between soft and rigid materials increases the fingertip force. The suggested principles were verified using the finite element methods. The improved performance of the hybrid gripper was verified experimentally and compared with the performance of a conventional SPAs. The ability of the hybrid gripper to grasp different objects was evaluated and was applied in a teleoperated system.
Finger ECG signal for user authentication: Usability and performance
(2013)

Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.
The future of parking: A survey on automated valet parking with an outlook on high density parking
(2017)

In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.
Web Application Migration with Closure Reconstruction
(2017)

Due to its high portability and simplicity, web application (app) based on HTML/JavaScript/CSS has been widely used for various smart-device platforms. To take advantage of its wide platform pool, a new idea called app migration has been proposed for the web platform. Web app migration is a framework to serialize a web app running on a device and restore it in another device to continue its execution. In JavaScript semantics, one of the language features that does not allow easy app migration is a closure. A JavaScript function can access variables defined in its outer function even if the execution of the outer function is terminated. It is allowed because the inner function is created as a closure such that it contains the outer function’s environment. This feature is widely used in web app development because it is the most common way to implement data encapsulation in web programming. Closures are not easy to serialize because environments can be shared by a number of closures and environments can be created in a nested way. In this paper, we propose a novel approach to fully serialize closures. We created mechanisms to extract information from a closure’s environment through the JavaScript engine and to serialize the information in a proper order so that the original relationship between closures and environments can be restored properly. We implemented our mechanism on the WebKit browser and successfully migrated Octane benchmarks and seven real web apps which heavily exploit closures. We also show that our mechanism works correctly even for some extreme, closure-heavy cases.
A 0 . 5V Ultra-Low-Power OTA With Improved Gain and Bandwidth
(2014)

In the context of ultra-low-power and ultra-low-voltage operational transconductance amplifiers (OTAs), there is the inconvenience of low unity-gain frequency and DC gain. In this paper, we presented a 0.5-V improved ultra-low-power OTA using the transistors operate in weak inversion. The proposed topology based on a bulk-driven input differential pair employed an under-weak-inversion gain-stage in the Miller capacitor feedback path to improve the “pole-splitting” effect. Simulations in a standard 0.18 μm CMOS process resulted on considerable enhancement in the unity-gain bandwidth and the DC gain as well. The topology presents rail-to-rail input and output swings and consumes only 1 μW.
Performance analysis of supervised machine learning algorithms for text classification
(2016)

The demand of text classification is growing significantly in web searching, data mining, web ranking, recommendation systems and so many other fields of information and technology. This paper illustrates the text classification process on different dataset using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applied these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analysis the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.
A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0
(2012)

A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term ‘‘ontology’’ is generally defined and described with regard to the knowledge domain of quality risk management. To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated. 2012 Elsevier Ltd. All rights reserved.
Low-voltage low-power fast-settling CMOS operational transconductance amplifiers for switched-capacitor applications
(2003)

This paper presents a new fully differential operational transconductance amplifier (OTA) for low-voltage and fast-settling switched-capacitor circuits in digital CMOS technology. The proposed two-stage OTA is a hybrid class A/AB that combines a folded cascode as the first stage with active current mirrors as the second stage. It employs a hybrid cascode compensation scheme, merged Ahuja and improved Ahuja style compensations, for fast settling.
Focal loss dense detector for vehicle surveillance
(2018)

Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.
Online Parameter Estimation Technique for Adaptive Control Applications of Interior PM Synchronous Motor Drives
(2016)

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.
Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition
(2017)

In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.
Sentence level emotion recognition based on decisions from subsentence segments
(2011)

Emotion recognition from speech plays an important role in developing affective and intelligent systems. This study investigates sentence-level emotion recognition. We propose to use a two-step approach to leverage information from subsentence segments for sentence level decision. First we use a segment level emotion classifier to generate predictions for segments within a sentence. A second component combines the predictions from these segments to obtain a sentence level decision. We evaluate different segment units (words, phrases, time-based segments) and different decision combination methods (majority vote, average of probabilities, and a Gaussian Mixture Model (GMM)). Our experimental results on two different data sets show that our proposed method significantly outperforms the standard sentence-based classification approach. In addition, we find that using time-based segments achieves the best performance, and thus no speech recognition or alignment is needed when using our method, which is important to develop language independent emotion recognition systems.
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
(2016)

The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.
Learning Character-level Representations for Part-of-Speech Tagging
(2014)

Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce stateof-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.
Character-based Neural Machine Translation
(2015)

We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.